US9248406B2 - On-line performance management of membrane separation process - Google Patents

On-line performance management of membrane separation process Download PDF

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US9248406B2
US9248406B2 US12/856,249 US85624910A US9248406B2 US 9248406 B2 US9248406 B2 US 9248406B2 US 85624910 A US85624910 A US 85624910A US 9248406 B2 US9248406 B2 US 9248406B2
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membrane
estimated
cleaning
plant
fouling
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US20110035195A1 (en
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Senthilmurugan Subbiah
Babji Buddhi Srinivasa
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ABB Schweiz AG
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01D—SEPARATION
    • B01D61/00—Processes of separation using semi-permeable membranes, e.g. dialysis, osmosis or ultrafiltration; Apparatus, accessories or auxiliary operations specially adapted therefor
    • B01D61/02—Reverse osmosis; Hyperfiltration ; Nanofiltration
    • B01D61/12—Controlling or regulating
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01D—SEPARATION
    • B01D61/00—Processes of separation using semi-permeable membranes, e.g. dialysis, osmosis or ultrafiltration; Apparatus, accessories or auxiliary operations specially adapted therefor
    • B01D61/14—Ultrafiltration; Microfiltration
    • B01D61/22—Controlling or regulating
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01D—SEPARATION
    • B01D65/00—Accessories or auxiliary operations, in general, for separation processes or apparatus using semi-permeable membranes
    • B01D65/02—Membrane cleaning or sterilisation ; Membrane regeneration
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F1/00—Treatment of water, waste water, or sewage
    • C02F1/44—Treatment of water, waste water, or sewage by dialysis, osmosis or reverse osmosis
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01D—SEPARATION
    • B01D2321/00—Details relating to membrane cleaning, regeneration, sterilization or to the prevention of fouling
    • B01D2321/40—Automatic control of cleaning processes
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F1/00—Treatment of water, waste water, or sewage
    • C02F1/44—Treatment of water, waste water, or sewage by dialysis, osmosis or reverse osmosis
    • C02F1/441—Treatment of water, waste water, or sewage by dialysis, osmosis or reverse osmosis by reverse osmosis
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F1/00—Treatment of water, waste water, or sewage
    • C02F1/44—Treatment of water, waste water, or sewage by dialysis, osmosis or reverse osmosis
    • C02F1/442—Treatment of water, waste water, or sewage by dialysis, osmosis or reverse osmosis by nanofiltration
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F1/00—Treatment of water, waste water, or sewage
    • C02F1/44—Treatment of water, waste water, or sewage by dialysis, osmosis or reverse osmosis
    • C02F1/444—Treatment of water, waste water, or sewage by dialysis, osmosis or reverse osmosis by ultrafiltration or microfiltration
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F2103/00—Nature of the water, waste water, sewage or sludge to be treated
    • C02F2103/08—Seawater, e.g. for desalination
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F2209/00—Controlling or monitoring parameters in water treatment
    • C02F2209/001—Upstream control, i.e. monitoring for predictive control
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F2209/00—Controlling or monitoring parameters in water treatment
    • C02F2209/003—Downstream control, i.e. outlet monitoring, e.g. to check the treating agents, such as halogens or ozone, leaving the process
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F2209/00—Controlling or monitoring parameters in water treatment
    • C02F2209/005—Processes using a programmable logic controller [PLC]
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F2209/00—Controlling or monitoring parameters in water treatment
    • C02F2209/03—Pressure
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F2209/00—Controlling or monitoring parameters in water treatment
    • C02F2209/05—Conductivity or salinity
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F2209/00—Controlling or monitoring parameters in water treatment
    • C02F2209/40—Liquid flow rate
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F2303/00—Specific treatment goals
    • C02F2303/16—Regeneration of sorbents, filters
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A20/00—Water conservation; Efficient water supply; Efficient water use
    • Y02A20/124—Water desalination
    • Y02A20/131—Reverse-osmosis

Definitions

  • the present disclosure relates to on-line performance monitoring, such as monitoring of reverse osmosis/nanofiltration plants by analyzing physical parameters of a membrane using a membrane transport phenomenological model.
  • RO/NF/UF Reverse Osmosis/Nanofiltration/Ultrafiltration
  • RO/NF/UF is a pressure driven membrane separation process used in various industries such as desalination, wastewater treatment and chemical manufacturing.
  • RO/NF/UF is used in plants to produce potable water from sea/brackish water.
  • high pressure is applied on the feed side of the membrane to overcome the osmotic pressure of solute and cause transport of the solvent from the feed side to a permeate side and solute accumulates near the membrane surface.
  • concentration of the solute near the membrane surface increases gradually over a period, adversely affecting the performance of membrane. This phenomena is called concentration polarization.
  • concentration polarization is inversely proportional to the feed velocity across the membrane module.
  • cleaning of the membrane is carried out in at least two ways; either based on a pressure drop between the feed and reject being more than a threshold value, or at predetermined fixed periodic intervals as per a recommendation by a membrane manufacturer.
  • the membrane may get damaged due to permanent fouling
  • membrane cleaning is independent of the actual fouling taking place in the membrane modules.
  • Mohamad Amin Saad [16] extended the ASTM method to measure “Fouling Monitor” (FM) to monitor the performance of an RO plant.
  • the FM is defined as a percentage difference between the normalized flux at design conditions and actual flux at the operating conditions of the RO plant.
  • a cleaning scheduling of a membrane is arrived at based on the value of the FM.
  • This method cannot predict the fouling of the membrane based on the operating conditions before normalized flux deviates from a design value.
  • the method based on normalized flux may not be sufficient to predict fouling of a membrane accurately.
  • Nalco chemical company [18-27] has developed a method for monitoring the performance of a membrane separation process. As per the method, a tracer is injected in the feed stream and the concentration of tracer in outlet streams was estimated experimentally by using external sensors. The tracer concentrations in the feed and the outlet streams are used to monitor the fouling taking place in the membrane separation processes. This technique involves external sensors and tracer injection systems for implementation.
  • Models proposed for charged membranes are developed by considering the chemical and physical properties of the solute and membrane such as solute size, solute charge, pore size of membrane and charge of membrane etc.
  • models based on the irreversible thermodynamics [4, 5] are developed by considering the membrane as a black box which has fluxes (permeate and solute flux) corresponding to the driving forces (pressure difference and concentration difference) of the transport process.
  • the phenomenological constants are used to correlate flux and driving force, and physical parameters of the membrane are derived from these phenomenological constants.
  • irreversible thermodynamic models the physical parameters of the membrane can be estimated for experimental data without knowing properties of membrane and solute.
  • Soltanieh and Gill [10] compared the performance of the SK model and the KK model and observed that at no fouling condition, the membrane physical parameters of the KK model were found to be a function of feed concentration, while SK model parameters were found to be constant with respect to feed concentration.
  • Several authors [11] compared the Solution Diffusion (SD) model with the SK model and concluded that the SK model predicts better than the SD model.
  • SD Solution Diffusion
  • Murthy and Gupta [12] proposed new a model, namely a Combined Film Spiegler-Kedem (CFSK) model, by including both membrane transport and concentration polarization effects. They concluded that CFSK model predictions are better than other models available in literature. Senthilmurugan et al [13] and Abhijit et al., [14] extended the CFSK model to spiral wound and hollow fiber modules respectively, and validated the models with experimental data with good results.
  • CFSK Combined Film Spiegler-Kedem
  • a method for real time performance management of membrane separation processes comprising: predicting a state of fouling of a membrane based on an estimation of a time varying physical parameter of the membrane from plant data; and scheduling cleaning of the membrane based on a comparison of an estimated time varying physical parameter with a pre-defined threshold value.
  • a system for real time performance management of a membrane separation process by performing a computer implemented program on a computer to implement a method comprising: predicting a state of fouling based on estimation of a time varying physical parameter of a membrane from plant data; and scheduling cleaning of the membrane based on a comparison of an estimated time varying physical parameter with a pre-defined threshold value.
  • a system for real time estimation of a time varying physical parameter of a membrane separation process from plant data, comprising: means to measure a plant process variable in real-time; means to store a real-time measurement of plant operation data in a computer based control system; means to process the plant operation data stored in the computer based control system to remove noise; means to estimate a physical parameter of a membrane using a mathematical model; and means to store an estimated physical parameter in the computer based control system.
  • FIG. 1 is schematic of an exemplary RO/NF/UF plant with an associated instrumentation and control system
  • FIG. 2 is an exemplary schematic of on-line performance monitoring system of an RO/NF/UF plant.
  • FIG. 3 is an exemplary schematic of a mathematical model.
  • Exemplary embodiments can implement an on-line method that can analyze available plant data in terms of fouling of membranes and suggest an appropriate membrane cleaning schedule to plant operators to maintain the performance of the RO plant and also extend the life of the membrane. So far, such on-line performance monitoring methods based on a membrane transport phenomenological model have not been reported for RO/NF/UF plants and the present disclosure is aimed at filling such a gap.
  • an exemplary method for real-time estimation of a state of fouling and cleaning scheduling for RO/NF/UF plant includes periodically executing the following steps: (i) using a phenomenological model to calculate the performance of an RO/NF/UF plant; (ii) on-line estimation of a membrane transport parameter of a phenomenological model at periodic intervals; and (iii) analysis of the membrane transport parameter to determine the state of the fouling of the membrane.
  • the present disclosure provides for performance monitoring of a membrane unit through online analysis of physical parameters of the phenomenological model of the membrane transport process. This method provides information about the time varying rate of fouling in a membrane unit, which can be used in scheduling of the membrane cleaning.
  • a proposed on-line performance monitoring method includes:
  • FIG. 1 illustrates a schematic of an RO/NF/UF plant with associated instrumentation and plant control system
  • the RO/NF/UF based desalination plant has following streams, namely feed, reject and permeate streams.
  • the feed is pretreated 2 before being pumped to RO/NF/UF membrane module through high pressure pump 3 .
  • the properties of the feed stream such as conductivity, pressure and flow rate are measured by corresponding sensors 4 , 5 , 6 .
  • the RO Modules network 7 is connected to the sensors 6 and 8 , the RO/NF/UF membrane module purifies the feed water and purified water is collected at a permeate end and concentrated water is collected at a reject end.
  • the process variables such as reject flow rate and pressure are measured at corresponding sensors 8 , 9 .
  • other process variables such as permeate flow rate and conductivity are measured by corresponding sensors 10 , 11 .
  • This measured data from sensors are stored in plant control system 1 . These measurements are carried under two conditions such as (i) normal operating conditions and (2) introducing at least one disturbance such as a step change in any one process variable.
  • FIG. 2 illustrates an exemplary schematic of the online performance monitoring system of an RO/NF/UF plant using mathematical model 22 and analysis of the estimated model parameters, which change with time depending upon the plant operating conditions.
  • the parameter estimation can be carried out by minimizing the error between the predicted and measured process variable under normal operating conditions.
  • the error minimization can be performed by a non-linear optimization technique.
  • Further estimated model parameters are used to validate the model using measured process variables.
  • FIG. 3 illustrates an exemplary mathematical model of an RO/NF/UF plant.
  • the mathematical model for the membrane module will change depending upon the configuration of the module used in a plant, namely a Hollow Fiber (HF) module or a Spiral Wound (SW) module or a tubular module 23 .
  • HF Hollow Fiber
  • SW Spiral Wound
  • FIG. 3 illustrates an exemplary mathematical model of an RO/NF/UF plant.
  • the mathematical model for the membrane module will change depending upon the configuration of the module used in a plant, namely a Hollow Fiber (HF) module or a Spiral Wound (SW) module or a tubular module 23 .
  • HF Hollow Fiber
  • SW Spiral Wound
  • the permeate flow rate and solute concentration obtained from a given HF module can be predicted [14] by solving a set of equations which describe the mass transfer processes in the module. These equations namely, the membrane transport model, concentration polarization model, local solvent and solute mass balances are all applicable at any point within the permeator.
  • the system of coupled differential equations may be solved numerically using the finite difference method.
  • Equations of the same form are used in literature for estimating the mass transfer coefficients.
  • the values of ‘a’ and ‘b’ for a hollow fiber module have been reviewed by Masaaki Sekino [29] for an HFRO module.
  • L m is length of module (m).
  • ⁇ P pw P atm - ⁇ 0 w ⁇ ( d P b d y ) ⁇ d y
  • P R reject pressure (Pa)
  • L is length of spiral wound module (m)
  • w width of module with respect to number of wounds (m).
  • the above equations are solved using the method of finite differences.
  • the feed flow path (x direction) is divided into m segments while the permeate flow path (y direction) is divided into n segments.
  • the permeate flux, and concentration at local points of the membrane module can be estimated.
  • the overall permeate concentration and flow rate can be estimated by the following equations:
  • the exemplary lists of physical parameters 24 used in the model are:
  • the conductivity of permeate can be estimated from the permeate concentration.
  • the above described mathematical models are used in the present method to describe the physical phenomena occurring in membrane separation processes.
  • the models include parameters such as solute permeability, hydrodynamic permeability and membrane reflection coefficient to characterize the fouling phenomena.
  • These model parameters are time varying in nature and are estimated periodically from the RO plant data such as flow rate, temperature, pressure and quality of feed, reject and permeate. Analysis of these estimated parameters will indicate the rate of fouling taking place in the RO plant and the cleaning of the membrane is recommended whenever the values of these parameters exceed a pre-defined threshold value.

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  • Engineering & Computer Science (AREA)
  • Water Supply & Treatment (AREA)
  • Chemical & Material Sciences (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Hydrology & Water Resources (AREA)
  • Environmental & Geological Engineering (AREA)
  • Organic Chemistry (AREA)
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  • Separation Using Semi-Permeable Membranes (AREA)
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EP3851181A1 (fr) * 2020-01-17 2021-07-21 Linde GmbH Procédé de surveillance d'un dispositif technique à membrane
CN113274885B (zh) * 2021-03-24 2023-02-07 重庆工商大学 应用于膜法污水处理的膜污染智能化预警方法
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US9737858B2 (en) * 2011-12-23 2017-08-22 Abb Schweiz Ag Method and a system for monitoring and control of fouling and optimization thereof of two side membrane fouling process
EP3685908A1 (fr) 2019-01-22 2020-07-29 NOV Process & Flow Technologies AS Détection de type d'encrassement
WO2020152100A1 (fr) 2019-01-22 2020-07-30 Nov Process & Flow Technologies As Détection de type d'encrassement
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